更换文档检测模型
This commit is contained in:
234
paddle_detection/ppdet/data/source/voc.py
Normal file
234
paddle_detection/ppdet/data/source/voc.py
Normal file
@@ -0,0 +1,234 @@
|
||||
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
from ppdet.core.workspace import register, serializable
|
||||
|
||||
from .dataset import DetDataset
|
||||
|
||||
from ppdet.utils.logger import setup_logger
|
||||
logger = setup_logger(__name__)
|
||||
|
||||
|
||||
@register
|
||||
@serializable
|
||||
class VOCDataSet(DetDataset):
|
||||
"""
|
||||
Load dataset with PascalVOC format.
|
||||
|
||||
Notes:
|
||||
`anno_path` must contains xml file and image file path for annotations.
|
||||
|
||||
Args:
|
||||
dataset_dir (str): root directory for dataset.
|
||||
image_dir (str): directory for images.
|
||||
anno_path (str): voc annotation file path.
|
||||
data_fields (list): key name of data dictionary, at least have 'image'.
|
||||
sample_num (int): number of samples to load, -1 means all.
|
||||
label_list (str): if use_default_label is False, will load
|
||||
mapping between category and class index.
|
||||
allow_empty (bool): whether to load empty entry. False as default
|
||||
empty_ratio (float): the ratio of empty record number to total
|
||||
record's, if empty_ratio is out of [0. ,1.), do not sample the
|
||||
records and use all the empty entries. 1. as default
|
||||
repeat (int): repeat times for dataset, use in benchmark.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
dataset_dir=None,
|
||||
image_dir=None,
|
||||
anno_path=None,
|
||||
data_fields=['image'],
|
||||
sample_num=-1,
|
||||
label_list=None,
|
||||
allow_empty=False,
|
||||
empty_ratio=1.,
|
||||
repeat=1):
|
||||
super(VOCDataSet, self).__init__(
|
||||
dataset_dir=dataset_dir,
|
||||
image_dir=image_dir,
|
||||
anno_path=anno_path,
|
||||
data_fields=data_fields,
|
||||
sample_num=sample_num,
|
||||
repeat=repeat)
|
||||
self.label_list = label_list
|
||||
self.allow_empty = allow_empty
|
||||
self.empty_ratio = empty_ratio
|
||||
|
||||
def _sample_empty(self, records, num):
|
||||
# if empty_ratio is out of [0. ,1.), do not sample the records
|
||||
if self.empty_ratio < 0. or self.empty_ratio >= 1.:
|
||||
return records
|
||||
import random
|
||||
sample_num = min(
|
||||
int(num * self.empty_ratio / (1 - self.empty_ratio)), len(records))
|
||||
records = random.sample(records, sample_num)
|
||||
return records
|
||||
|
||||
def parse_dataset(self, ):
|
||||
anno_path = os.path.join(self.dataset_dir, self.anno_path)
|
||||
image_dir = os.path.join(self.dataset_dir, self.image_dir)
|
||||
|
||||
# mapping category name to class id
|
||||
# first_class:0, second_class:1, ...
|
||||
records = []
|
||||
empty_records = []
|
||||
ct = 0
|
||||
cname2cid = {}
|
||||
if self.label_list:
|
||||
label_path = os.path.join(self.dataset_dir, self.label_list)
|
||||
if not os.path.exists(label_path):
|
||||
raise ValueError("label_list {} does not exists".format(
|
||||
label_path))
|
||||
with open(label_path, 'r') as fr:
|
||||
label_id = 0
|
||||
for line in fr.readlines():
|
||||
cname2cid[line.strip()] = label_id
|
||||
label_id += 1
|
||||
else:
|
||||
cname2cid = pascalvoc_label()
|
||||
|
||||
with open(anno_path, 'r') as fr:
|
||||
while True:
|
||||
line = fr.readline()
|
||||
if not line:
|
||||
break
|
||||
img_file, xml_file = [os.path.join(image_dir, x) \
|
||||
for x in line.strip().split()[:2]]
|
||||
if not os.path.exists(img_file):
|
||||
logger.warning(
|
||||
'Illegal image file: {}, and it will be ignored'.format(
|
||||
img_file))
|
||||
continue
|
||||
if not os.path.isfile(xml_file):
|
||||
logger.warning(
|
||||
'Illegal xml file: {}, and it will be ignored'.format(
|
||||
xml_file))
|
||||
continue
|
||||
tree = ET.parse(xml_file)
|
||||
if tree.find('id') is None:
|
||||
im_id = np.array([ct])
|
||||
else:
|
||||
im_id = np.array([int(tree.find('id').text)])
|
||||
|
||||
objs = tree.findall('object')
|
||||
im_w = float(tree.find('size').find('width').text)
|
||||
im_h = float(tree.find('size').find('height').text)
|
||||
if im_w < 0 or im_h < 0:
|
||||
logger.warning(
|
||||
'Illegal width: {} or height: {} in annotation, '
|
||||
'and {} will be ignored'.format(im_w, im_h, xml_file))
|
||||
continue
|
||||
|
||||
num_bbox, i = len(objs), 0
|
||||
gt_bbox = np.zeros((num_bbox, 4), dtype=np.float32)
|
||||
gt_class = np.zeros((num_bbox, 1), dtype=np.int32)
|
||||
gt_score = np.zeros((num_bbox, 1), dtype=np.float32)
|
||||
difficult = np.zeros((num_bbox, 1), dtype=np.int32)
|
||||
for obj in objs:
|
||||
cname = obj.find('name').text
|
||||
|
||||
# user dataset may not contain difficult field
|
||||
_difficult = obj.find('difficult')
|
||||
_difficult = int(
|
||||
_difficult.text) if _difficult is not None else 0
|
||||
|
||||
x1 = float(obj.find('bndbox').find('xmin').text)
|
||||
y1 = float(obj.find('bndbox').find('ymin').text)
|
||||
x2 = float(obj.find('bndbox').find('xmax').text)
|
||||
y2 = float(obj.find('bndbox').find('ymax').text)
|
||||
x1 = max(0, x1)
|
||||
y1 = max(0, y1)
|
||||
x2 = min(im_w - 1, x2)
|
||||
y2 = min(im_h - 1, y2)
|
||||
if x2 > x1 and y2 > y1:
|
||||
gt_bbox[i, :] = [x1, y1, x2, y2]
|
||||
gt_class[i, 0] = cname2cid[cname]
|
||||
gt_score[i, 0] = 1.
|
||||
difficult[i, 0] = _difficult
|
||||
i += 1
|
||||
else:
|
||||
logger.warning(
|
||||
'Found an invalid bbox in annotations: xml_file: {}'
|
||||
', x1: {}, y1: {}, x2: {}, y2: {}.'.format(
|
||||
xml_file, x1, y1, x2, y2))
|
||||
gt_bbox = gt_bbox[:i, :]
|
||||
gt_class = gt_class[:i, :]
|
||||
gt_score = gt_score[:i, :]
|
||||
difficult = difficult[:i, :]
|
||||
|
||||
voc_rec = {
|
||||
'im_file': img_file,
|
||||
'im_id': im_id,
|
||||
'h': im_h,
|
||||
'w': im_w
|
||||
} if 'image' in self.data_fields else {}
|
||||
|
||||
gt_rec = {
|
||||
'gt_class': gt_class,
|
||||
'gt_score': gt_score,
|
||||
'gt_bbox': gt_bbox,
|
||||
'difficult': difficult
|
||||
}
|
||||
for k, v in gt_rec.items():
|
||||
if k in self.data_fields:
|
||||
voc_rec[k] = v
|
||||
|
||||
if len(objs) == 0:
|
||||
empty_records.append(voc_rec)
|
||||
else:
|
||||
records.append(voc_rec)
|
||||
|
||||
ct += 1
|
||||
if self.sample_num > 0 and ct >= self.sample_num:
|
||||
break
|
||||
assert ct > 0, 'not found any voc record in %s' % (self.anno_path)
|
||||
logger.debug('{} samples in file {}'.format(ct, anno_path))
|
||||
if self.allow_empty and len(empty_records) > 0:
|
||||
empty_records = self._sample_empty(empty_records, len(records))
|
||||
records += empty_records
|
||||
self.roidbs, self.cname2cid = records, cname2cid
|
||||
|
||||
def get_label_list(self):
|
||||
return os.path.join(self.dataset_dir, self.label_list)
|
||||
|
||||
|
||||
def pascalvoc_label():
|
||||
labels_map = {
|
||||
'aeroplane': 0,
|
||||
'bicycle': 1,
|
||||
'bird': 2,
|
||||
'boat': 3,
|
||||
'bottle': 4,
|
||||
'bus': 5,
|
||||
'car': 6,
|
||||
'cat': 7,
|
||||
'chair': 8,
|
||||
'cow': 9,
|
||||
'diningtable': 10,
|
||||
'dog': 11,
|
||||
'horse': 12,
|
||||
'motorbike': 13,
|
||||
'person': 14,
|
||||
'pottedplant': 15,
|
||||
'sheep': 16,
|
||||
'sofa': 17,
|
||||
'train': 18,
|
||||
'tvmonitor': 19
|
||||
}
|
||||
return labels_map
|
||||
Reference in New Issue
Block a user